Linear discriminant analysis

About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.

Comparing wavelet transforms for recognizing cardiac patterns

Abstract: The authors' study made use of wavelet transforms to describe and recognize isolated cardiac beats. The choice of the wavelet family as well as the selection of the analyzing function into these families are discussed. The criterion used in the first case was the correct classification rate, and in the second case, the correlation coefficient between the original pattern and the reconstructed one. Two types of description have been considered-the energy-based representation and the extrema distribution estimated at each decomposition level-and their quality has been assessed by using principal component analysis. Their capability of discrimination between normal, premature ventricular contraction, and ischemic beats has been studied by means of linear discriminant analysis. This work leads also, for the problem at hand, to the identification of the most relevant resolution levels. >

A Review of Bankruptcy Prediction Studies: 1930-Present

Abstract: One of the most well-known bankruptcy prediction models was developed by Altman [1968) using multivariate discriminant analysis. Since Altman 5 model, a multitude of bankruptcy prediction models have flooded the literature. The primary goal of this paper is to summarize and analyze existing research on bankruptcy prediction studies in order to faCilitate more productive future research in this area. This paper traces the literature on bankruptcy prediction from the 19305, when studies focused on the use of simple ratio analysis to predict future bankruptcy, to present. The authors discuss how bankruptcy prediction studieshave evolved, highlighting the different methods, number and variety of factors, and specific uses of models. Analysis of 165 bankruptcy prediction studies published from 1965 to present reveals trends in model development. Forexample, discriminant analysis was the primary method used to develop models in the 19605 and 19705. Investigation of model type by decade shows that the primary method began to shift to logit analysis and neural networks in the 19805 and 19905. The number of factors utilized in models is also analyzed by decade, showing that the average has varied over time but remains around 10 overall. Analysis of accuracy of the models suggests that multivariate discriminant analysis and neural networks are the most promising methods for bankruptcy prediction models. The findings also suggest that higher model accuracy is not guaranteed with a greater number of factors. Some models with two factors are fust as capable of accurate prediction as models with 21 factors.